Broken link fixed

Author: pritamso

articles/machine-learning/v1/how-to-attach-compute-targets.md

@@ -54,14 +54,14 @@ To use compute targets managed by Azure Machine Learning, see:
 
 ## What's a compute target?
 
-With Azure Machine Learning, you can train your model on various resources or environments, collectively referred to as [__compute targets__](concept-azure-machine-learning-architecture.md#compute-targets). A compute target can be a local machine or a cloud resource, such as an Azure Machine Learning Compute, Azure HDInsight, or a remote virtual machine.  You also use compute targets for model deployment as described in ["Where and how to deploy your models"](../how-to-deploy-and-where.md).
+With Azure Machine Learning, you can train your model on various resources or environments, collectively referred to as [__compute targets__](concept-azure-machine-learning-architecture.md#compute-targets). A compute target can be a local machine or a cloud resource, such as an Azure Machine Learning Compute, Azure HDInsight, or a remote virtual machine.  You also use compute targets for model deployment as described in ["Where and how to deploy your models"](../how-to-deploy-managed-online-endpoints.md).
 
 
 ## Local computer
 
 When you use your local computer for **training**, there is no need to create a compute target.  Just [submit the training run](../how-to-set-up-training-targets.md) from your local machine.
 
-When you use your local computer for **inference**, you must have Docker installed. To perform the deployment, use [LocalWebservice.deploy_configuration()](/python/api/azureml-core/azureml.core.webservice.local.localwebservice#deploy-configuration-port-none-) to define the port that the web service will use. Then use the normal deployment process as described in [Deploy models with Azure Machine Learning](../how-to-deploy-and-where.md).
+When you use your local computer for **inference**, you must have Docker installed. To perform the deployment, use [LocalWebservice.deploy_configuration()](/python/api/azureml-core/azureml.core.webservice.local.localwebservice#deploy-configuration-port-none-) to define the port that the web service will use. Then use the normal deployment process as described in [Deploy models with Azure Machine Learning](../how-to-deploy-managed-online-endpoints.md).
 
 ## Remote virtual machines
 
@@ -370,5 +370,5 @@ See these notebooks for examples of training with various compute targets:
 * Use the compute resource to [configure and submit a training run](../how-to-set-up-training-targets.md).
 * [Tutorial: Train and deploy a model](../tutorial-train-deploy-notebook.md) uses a managed compute target to  train a model.
 * Learn how to [efficiently tune hyperparameters](../how-to-tune-hyperparameters.md) to build better models.
-* Once you have a trained model, learn [how and where to deploy models](../how-to-deploy-and-where.md).
+* Once you have a trained model, learn [how and where to deploy models](../how-to-deploy-managed-online-endpoints.md).
 * [Use Azure Machine Learning with Azure Virtual Networks](../how-to-network-security-overview.md)